基于ISL0算法的码间干扰稀疏多径信道估计

doi:10.3969/j.issn.1000-436x.2014.05.017

Abstract

Abstract:

A smoothed L0 (SL0) algorithm based on compressed sensing proposed in previous works for inter symbol in-terference (ISI) sparse channel estimation. But this method has “notched effect” due to the negative iterative gradient direction. Moreover, the “steep nature” of cost function in SL0 is not steep enough, leading to channel estimation errors and make convergence results not the most optimal. The lagrange multipliers and newton method were combined to op-timize SL0 algorithm in order to obtain a more rapid and efficient signal reconstruction algorithm termed as an improved smoothed L0 (ISL0). The channel state information (CSI) of the sparse multi-path channel was obtained and analysis of reconstructed signal deviation, mean squared error (MSE) in the perspective of iterations and signal-to-noise ratio (SNR) as well as the iteration time and ISI equalization performance were also done. Furthermore, the superiority of ISL0 has been verified by computer simulation. Real-time simulation results clearly show that the ISL0 algorithm can estimate the ISI sparse channel much better. Compared with CoSaMP, SL0 and some other algorithms, the ISL0 algorithm can greatly improve the performance of system in the same channel environments.

Key words: compressed-sensing, linear program, non-convex optimization, ISL0 algorithm, sparse restoration

Ting LIU,Jie ZHOU,CHIJiu-he JU. ISI sparse channel estimation based on ISL0 algorithm[J]. Journal on Communications, 2014, 35(5): 124-133.

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References 22

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环境变量	取值
输入信号	等概率随机向量信号{±}序列
信号长度	100
信道长度	300
非零抽头	30
信道特征	满足零均值，方差为1的高斯分布
加性噪声	白高斯序列，方差为0.01

环境变量	取值
输入信号	等概率随机向量信号{±}序列
信号长度	100
信道特征	时变系统
加性噪声	白高斯序列，方差为0.01

模式	描述
模式1	非零抽头个数为20，随机分布
	稀疏信道，迭代250次
模式2	奇数抽头设为1，偶数抽头设为-1稀疏信道，迭代250次
模式3	奇数抽头设为-1，偶数抽头设为1稀疏信道，迭代250次

ISI sparse channel estimation based on ISL0 algorithm

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